a heap is a specialized tree-based data structure that satisfies the heap property: if P is a parent node of C, then the key(the value) of P is either greater than or equal to (in a max heap) or less than or equal to (in a min heap) the key of C.^[1] The node at the "top" of the heap (with no parents) is called the root node.

The heap is one maximally efficient implementation of an abstract data type called a priority queue, and in fact priority queues are often referred to as "heaps", regardless of how they may be implemented. A common implementation of a heap is the binary heap, in which the tree is a binary tree (see figure). The heap data structure, specifically the binary heap, was introduced by J. W. J. Williams in 1964, as a data structure for the heapsort sorting algorithm.^[2] Heaps are also crucial in several efficient graph algorithms such as Dijkstra's algorithm. In a heap, the highest (or lowest) priority element is always stored at the root. A heap is not a sorted structure and can be regarded as partially ordered. As visible from the heap-diagram, there is no particular relationship among nodes on any given level, even among the siblings. When a heap is a complete binary tree, it has a smallest possible height—a heap with N nodes and for each node a branches always has log_a N height. A heap is a useful data structure when you need to remove the object with the highest (or lowest) priority.

https://en.wikipedia.org/wiki/Heap_(data_structure)